Thursday, 9 February 2017

Causal Bayesian networks are at the heart of a major new collaborative research project led by Australian University Monash
- funded by the United States' Intelligence Advanced Research Projects
Activity (IARPA). The objective is to help intelligence analysts assess
the value of their information. IARPA was set up following the failure
of the US intelligence agencies to properly assess the correct levels of
threat posed by Al Qaeda in 2001 and Iraq in 2003.

The chief investigator at Monash, Kevin Korb, said in an interview in the Australian:

"..quantitative
rather than qualitative methods were crucial in judging the value of
intelligence.... more quantitative approaches could have helped contain
the ebola epidemic by making authorities appreciate the scale of the
problem months earlier. They could also build a better assessment of the
likelihood of events like gunfire between vessels in the South China
Sea, a substantial devaluation of the Venezuelan currency or a new
presidential aspirant in Egypt."

Norman Fenton and Martin Neil (both of Agena and Queen Mary University of London)
will be working on the project along with colleagues such as David
Lagnado and Ulrike Hahn at UCL. AgenaRisk will be used throughout the
project as the Bayesian network platform.

Wednesday, 8 February 2017

UPDATE 9 Feb 2017: Various Research Fellowship and PhD vacancies funded by this project are now advertised. See here.

Queen
Mary has been awarded a grant of £1,538,497 (Full economic cost
£1,923,122) from the EPSRC towards a major new collaborative project to
develop a new generation of intelligent medical decision support
systems. The project, called PAMBAYESIAN (Patient Managed
Decision-Support using Bayesian Networks) focuses on home-based and
wearable real-time monitoring systems for chronic conditions including
rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation. It
has the potential to improve the well-being of millions of people.

The project team includes researchers from both the
School of Electronic Engineering and Computer Science (EECS) and
clinical academics from the Barts and the London School of Medicine and
Dentistry (SMD). The collaboration is underpinned by extensive research
in EECS and SMD, with access to digital health firms that have extensive
experience developing patient engagement tools for clinical development
(BeMoreDigital, Mediwise, Rescon, SMART Medical, uMotif, IBM UK and
Hasiba Medical).

Patients with chronic diseases must take
day-to-day decisions about their care and rely on advice from medical
staff to do this. However, regular appointments with doctors or nurses
are expensive, inconvenient and not necessarily scheduled when needed.
Increasingly, we are seeing the use of low cost and highly portable
sensors that can measure a wide range of physiological values. Such
'wearable' sensors could improve the way chronic conditions are managed.
Patients could have more control over their own care if they wished;
doctors and nurses could monitor their patients without the expense and
inconvenience of visits, except when they are needed. Remote monitoring
of patients is already in use for some conditions but there are barriers
to its wider use: it relies too much on clinical staff to interpret the
sensor readings; patients, confused by the information presented, may
become more dependent on health professionals; remote sensor use may
then lead to an increase in medical assistance, rather than reduction.

The project seeks to overcome these barriers by addressing two key weaknesses of the current systems:

Their lack of intelligence. Intelligent systems that can help
medical staff in making decisions already exist and can be used for
diagnosis, prognosis and advice on treatments. One especially important
form of these systems uses belief or Bayesian networks, which show how
the relevant factors are related and allow beliefs, such as the presence
of a medical condition, to be updated from the available evidence.
However, these intelligent systems do not yet work easily with data
coming from sensors.

Any mismatch between the design of the technical system and the way the people - patients and professional - interact.

We will work on these two weaknesses together: patients and
medical staff will be involved from the start, enabling us to understand
what information is needed by each player and how to use the
intelligent reasoning to provide it.

The medical work will be centred on three case
studies, looking at the management of rheumatoid arthritis, diabetes in
pregnancy and atrial fibrillation (irregular heartbeat). These have been
chosen both because they are important chronic diseases and because
they are investigated by significant research groups in our Medical
School, who are partners in the project. This makes them ideal test beds
for the technical developments needed to realise our vision and allow
patients more autonomy in practice.

To advance the technology, we will design ways to
create belief networks for the different intelligent reasoning tasks,
derived from an overall model of medical knowledge relevant to the
diseases being managed. Then we will investigate how to run the
necessary algorithms on the small computers attached to the sensors that
gather the data as well as on the systems used by the healthcare team.
Finally, we will use the case studies to learn how the technical systems
can integrate smoothly into the interactions between patients and
health professionals, ensuring that information presented to patients is
understandable, useful and reduces demands on the care system while at
the same time providing the clinical team with the information they need
to ensure that patients are safe.

Martin Neil

About Me

Norman's experience in risk assessment covers application domains such as legal reasoning (he has been an expert witness in major criminal and civil cases), software project risk, medical decision-making, vehicle reliability, football prediction, transport systems, and financial services. Norman has published over 130 articles and 5 books on these subjects